Distributed opinion competition scheme with gradient-based neural network in social networks

Abstract

In the context of social networks becoming primary platforms for information dissemination and public discourse, understanding how opinions compete and reach consensus has become increasingly vital. This paper introduces a novel distributed competition model designed to elucidate the dynamics of opinion competitive behavior in social networks. The proposed model captures the development mechanism of various opinions, their appeal to individuals, and the impact of the social environment on their evolution. The model reveals that a subset of opinions ultimately prevails and is adopted. Key elements of social networks are quantified as parameters, with parameter variations representing the dynamics of opinions. Furthermore, a modified gradient-based neural network is designed as the evolutional law of the opinion, whose stability and convergence are confirmed by theoretical analysis. Additionally, experiments simulate real-world competitive scenarios, demonstrating practical applications for the model. This model can be widely applied to various filed in social networks, offering a new perspective for understanding and predicting competition phenomenon in complex social systems. Overall, this work provides a structured and systematic approach to understanding opinion dynamics, which greatly enhances our ability to analyze competitive behaviors and anticipate the outcomes of diverse viewpoints in social networks.

Keywords: k-winners-take-all (k-WTA); Distributed scheme; Opinion competition; Recurrent neural network (RNN); Social network.

Fig. 1

The schematic diagram of this paper.

Fig. 2

Opinion dynamics in social network. (a) The randomly generated initial distribution of opinions in social network. (b) The opinions dynamics within s. (c) The opinions dynamics within s.

Fig. 3

Simulative trajectories of variables in the proposed model. (a) The norm of fitness between opinion and . (b) The output of the k-WTA model. (c) The state of opinions in three dimensions.

Fig. 4

The outputs of the proposed model with various typologies. (a) A static topology whose connections are based on the fitness between opinions. (b) The transitional model whose connections are based on the fitness between opinions. (c) The transitional Erdős–Rényi model.